{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch, os\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torchvision.datasets as datasets\n",
    "import torch.utils.data as data\n",
    "import torchvision.transforms as transforms\n",
    "import torchvision.models as models\n",
    "from train_model import train_model\n",
    "from test_model import test_model\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = 'tiny-imagenet-200/'\n",
    "num_workers = {'train' : 100,'val'   : 0,'test'  : 0}\n",
    "data_transforms = {\n",
    "    'train': transforms.Compose([\n",
    "        transforms.ToTensor(),\n",
    "    ]),\n",
    "    'val': transforms.Compose([\n",
    "        transforms.ToTensor(),\n",
    "    ]),\n",
    "    'test': transforms.Compose([\n",
    "        transforms.ToTensor(),\n",
    "    ])\n",
    "}\n",
    "image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x]) \n",
    "                  for x in ['train', 'val','test']}\n",
    "dataloaders = {x: data.DataLoader(image_datasets[x], batch_size=100, shuffle=True, num_workers=num_workers[x])\n",
    "                  for x in ['train', 'val', 'test']}\n",
    "dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val', 'test']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load Resnet18\n",
    "model_ft = models.resnet18()\n",
    "#Finetune Final few layers to adjust for tiny imagenet input\n",
    "model_ft.avgpool = nn.AdaptiveAvgPool2d(1)\n",
    "model_ft.fc.out_features = 200\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "model_ft = model_ft.to(device)\n",
    "#Loss Function\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "# Observe that all parameters are being optimized\n",
    "optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Loss: 2.6119 Acc: 0.3876\n",
      "Val Loss: 3.6605 Acc: 0.2034\n",
      "\n",
      "Training complete in 8m 17s\n",
      "Best Validation Accuracy: 0.2104, Epoch: 8\n"
     ]
    }
   ],
   "source": [
    "#Train\n",
    "train_model(\"64_no_pre\",model_ft, dataloaders, dataset_sizes, criterion, optimizer_ft, num_epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load Resnet18\n",
    "model_ft = models.resnet18(True)\n",
    "#Finetune Final few layers to adjust for tiny imagenet input\n",
    "model_ft.avgpool = nn.AdaptiveAvgPool2d(1)\n",
    "model_ft.fc.out_features = 200\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "model_ft = model_ft.to(device)\n",
    "#Loss Function\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "# Observe that all parameters are being optimized\n",
    "optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Loss: 0.3296 Acc: 0.9187\n",
      "Val Loss: 2.7901 Acc: 0.4576\n",
      "\n",
      "Training complete in 8m 16s\n",
      "Best Validation Accuracy: 0.4702, Epoch: 5\n"
     ]
    }
   ],
   "source": [
    "#Train\n",
    "train_model(\"64_pre\",model_ft, dataloaders, dataset_sizes, criterion, optimizer_ft, num_epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = 'tiny-imagenet-200/'\n",
    "num_workers = {'train' : 100,'val'   : 0,'test'  : 0}\n",
    "data_transforms = {\n",
    "    'train': transforms.Compose([\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.4802, 0.4481, 0.3975], [0.2302, 0.2265, 0.2262]),\n",
    "    ]),\n",
    "    'val': transforms.Compose([\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.4802, 0.4481, 0.3975], [0.2302, 0.2265, 0.2262]),\n",
    "    ]),\n",
    "    'test': transforms.Compose([\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.4802, 0.4481, 0.3975], [0.2302, 0.2265, 0.2262]),\n",
    "    ])\n",
    "}\n",
    "image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x]) \n",
    "                  for x in ['train', 'val','test']}\n",
    "dataloaders = {x: data.DataLoader(image_datasets[x], batch_size=100, shuffle=True, num_workers=num_workers[x])\n",
    "                  for x in ['train', 'val', 'test']}\n",
    "dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val', 'test']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load Resnet18\n",
    "model_ft = models.resnet18(True)\n",
    "#Finetune Final few layers to adjust for tiny imagenet input\n",
    "model_ft.avgpool = nn.AdaptiveAvgPool2d(1)\n",
    "model_ft.fc.out_features = 200\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "model_ft = model_ft.to(device)\n",
    "#Loss Function\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "# Observe that all parameters are being optimized\n",
    "optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Loss: 0.3580 Acc: 0.9104\n",
      "Val Loss: 2.8279 Acc: 0.4496\n",
      "\n",
      "Training complete in 8m 24s\n",
      "Best Validation Accuracy: 0.4732, Epoch: 5\n"
     ]
    }
   ],
   "source": [
    "#Train\n",
    "train_model(\"64_norm\",model_ft, dataloaders, dataset_sizes, criterion, optimizer_ft, num_epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = 'tiny-imagenet-200/'\n",
    "num_workers = {'train' : 100,'val'   : 0,'test'  : 0}\n",
    "data_transforms = {\n",
    "    'train': transforms.Compose([\n",
    "        transforms.RandomHorizontalFlip(0.5),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.4802, 0.4481, 0.3975], [0.2302, 0.2265, 0.2262]),\n",
    "    ]),\n",
    "    'val': transforms.Compose([\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.4802, 0.4481, 0.3975], [0.2302, 0.2265, 0.2262]),\n",
    "    ]),\n",
    "    'test': transforms.Compose([\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.4802, 0.4481, 0.3975], [0.2302, 0.2265, 0.2262]),\n",
    "    ])\n",
    "}\n",
    "image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x]) \n",
    "                  for x in ['train', 'val','test']}\n",
    "dataloaders = {x: data.DataLoader(image_datasets[x], batch_size=100, shuffle=True, num_workers=num_workers[x])\n",
    "                  for x in ['train', 'val', 'test']}\n",
    "dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val', 'test']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load Resnet18\n",
    "model_ft = models.resnet18(True)\n",
    "#Finetune Final few layers to adjust for tiny imagenet input\n",
    "model_ft.avgpool = nn.AdaptiveAvgPool2d(1)\n",
    "model_ft.fc.out_features = 200\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "model_ft = model_ft.to(device)\n",
    "#Loss Function\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "# Observe that all parameters are being optimized\n",
    "optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Loss: 1.0078 Acc: 0.7325\n",
      "Val Loss: 2.2083 Acc: 0.5082\n",
      "\n",
      "Training complete in 8m 27s\n",
      "Best Validation Accuracy: 0.5082, Epoch: 10\n"
     ]
    }
   ],
   "source": [
    "#Train\n",
    "train_model(\"64_norm_flip\",model_ft, dataloaders, dataset_sizes, criterion, optimizer_ft, num_epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = 'tiny-imagenet-200/'\n",
    "num_workers = {'train' : 100,'val'   : 0,'test'  : 0}\n",
    "data_transforms = {\n",
    "    'train': transforms.Compose([\n",
    "        transforms.RandomRotation(20),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.4802, 0.4481, 0.3975], [0.2302, 0.2265, 0.2262]),\n",
    "    ]),\n",
    "    'val': transforms.Compose([\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.4802, 0.4481, 0.3975], [0.2302, 0.2265, 0.2262]),\n",
    "    ]),\n",
    "    'test': transforms.Compose([\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.4802, 0.4481, 0.3975], [0.2302, 0.2265, 0.2262]),\n",
    "    ])\n",
    "}\n",
    "image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x]) \n",
    "                  for x in ['train', 'val','test']}\n",
    "dataloaders = {x: data.DataLoader(image_datasets[x], batch_size=100, shuffle=True, num_workers=num_workers[x])\n",
    "                  for x in ['train', 'val', 'test']}\n",
    "dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val', 'test']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load Resnet18\n",
    "model_ft = models.resnet18(True)\n",
    "#Finetune Final few layers to adjust for tiny imagenet input\n",
    "model_ft.avgpool = nn.AdaptiveAvgPool2d(1)\n",
    "model_ft.fc.out_features = 200\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "model_ft = model_ft.to(device)\n",
    "#Loss Function\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "# Observe that all parameters are being optimized\n",
    "optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Loss: 1.4814 Acc: 0.6170\n",
      "Val Loss: 2.1820 Acc: 0.4976\n",
      "\n",
      "Training complete in 8m 29s\n",
      "Best Validation Accuracy: 0.49760000000000004, Epoch: 10\n"
     ]
    }
   ],
   "source": [
    "#Train\n",
    "train_model(\"64_norm_rotate\",model_ft, dataloaders, dataset_sizes, criterion, optimizer_ft, num_epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = 'tiny-imagenet-200/'\n",
    "num_workers = {'train' : 100,'val'   : 0,'test'  : 0}\n",
    "data_transforms = {\n",
    "    'train': transforms.Compose([\n",
    "        transforms.RandomRotation(20),\n",
    "        transforms.RandomHorizontalFlip(0.5),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.4802, 0.4481, 0.3975], [0.2302, 0.2265, 0.2262]),\n",
    "    ]),\n",
    "    'val': transforms.Compose([\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.4802, 0.4481, 0.3975], [0.2302, 0.2265, 0.2262]),\n",
    "    ]),\n",
    "    'test': transforms.Compose([\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.4802, 0.4481, 0.3975], [0.2302, 0.2265, 0.2262]),\n",
    "    ])\n",
    "}\n",
    "image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x]) \n",
    "                  for x in ['train', 'val','test']}\n",
    "dataloaders = {x: data.DataLoader(image_datasets[x], batch_size=100, shuffle=True, num_workers=num_workers[x])\n",
    "                  for x in ['train', 'val', 'test']}\n",
    "dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val', 'test']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load Resnet18\n",
    "model_ft = models.resnet18(True)\n",
    "#Finetune Final few layers to adjust for tiny imagenet input\n",
    "model_ft.avgpool = nn.AdaptiveAvgPool2d(1)\n",
    "model_ft.fc.out_features = 200\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "model_ft = model_ft.to(device)\n",
    "#Loss Function\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "# Observe that all parameters are being optimized\n",
    "optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Loss: 0.9826 Acc: 0.7331\n",
      "Val Loss: 2.1627 Acc: 0.5280\n",
      "\n",
      "Training complete in 16m 54s\n",
      "Best Validation Accuracy: 0.533, Epoch: 17\n"
     ]
    }
   ],
   "source": [
    "#Train\n",
    "train_model(\"64\",model_ft, dataloaders, dataset_sizes, criterion, optimizer_ft, num_epochs=20)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:pytorch_p36]",
   "language": "python",
   "name": "conda-env-pytorch_p36-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
